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Extracting Relevant Test Inputs From Bug Reports For Automatic Test Case Generation, Wendkuuni C. Ouédraogo, Laura Plein, Kader Kaboré, Andrew Habib, Jacques Klein, David Lo, Tegawende F. Bissyandé Apr 2024

Extracting Relevant Test Inputs From Bug Reports For Automatic Test Case Generation, Wendkuuni C. Ouédraogo, Laura Plein, Kader Kaboré, Andrew Habib, Jacques Klein, David Lo, Tegawende F. Bissyandé

Research Collection School Of Computing and Information Systems

The pursuit of automating software test case generation, particularly for unit tests, has become increasingly important due to the labor-intensive nature of manual test generation [6]. However, a significant challenge in this domain is the inability of automated approaches to generate relevant inputs, which compromises the efficacy of the tests [6].

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The Impact Of Bug Localization Based On Crash Report Mining: A Developers' Perspective, Marcos Medeiros, Uirá Kulesza, Roberta Coelho, Rodrigo Bonifacio, Christoph Treude, Eiji Adachi Barbosa Apr 2024

The Impact Of Bug Localization Based On Crash Report Mining: A Developers' Perspective, Marcos Medeiros, Uirá Kulesza, Roberta Coelho, Rodrigo Bonifacio, Christoph Treude, Eiji Adachi Barbosa

Research Collection School Of Computing and Information Systems

Developers often use crash reports to understand the root cause of bugs. However, locating the buggy source code snippet from such information is a challenging task, mainly when the log database contains many crash reports. To mitigate this issue, recent research has proposed and evaluated approaches for grouping crash report data and using stack trace information to locate bugs. The effectiveness of such approaches has been evaluated by mainly comparing the candidate buggy code snippets with the actual changed code in bug-fix commits—which happens in the context of retrospective repository mining studies. Therefore, the existing literature still lacks discussing the …

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Githubinclusifier: Finding And Fixing Non-Inclusive Language In Github Repositories, Liam Todd, John Grundy, Christoph Treude Apr 2024

Githubinclusifier: Finding And Fixing Non-Inclusive Language In Github Repositories, Liam Todd, John Grundy, Christoph Treude

Research Collection School Of Computing and Information Systems

Non-inclusive language in software artefacts has been recognised as a serious problem. We describe a tool to find and fix non-inclusive language in a variety of GitHub repository artefacts. These include various README files, PDFs, code comments, and code. A wide variety of non-inclusive language including racist, ageist, ableist, violent and others are located and issues created, tagging the artefacts for checking. Suggested fixes can be generated using third-party LLM APIs, and approved changes made to documents, including code refactorings, and committed to the repository. The tool and evaluation data are available from: https://github. com/LiamTodd/github-inclusifier

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My Github Sponsors Profile Is Live!": Investigating The Impact Of Twitter/X Mentions On Github Sponsors, Youmei Fan, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto Apr 2024

My Github Sponsors Profile Is Live!": Investigating The Impact Of Twitter/X Mentions On Github Sponsors, Youmei Fan, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

GitHub Sponsors was launched in 2019, enabling donations to opensource software developers to provide financial support, as per GitHub’s slogan: “Invest in the projects you depend on”. However, a 2022 study on GitHub Sponsors found that only two-fifths of developers who were seeking sponsorship received a donation. The study found that, other than internal actions (such as offering perks to sponsors), developers had advertised their GitHub Sponsors profiles on social media, such as Twitter (also known as X). Therefore, in this work, we investigate the impact of tweets that contain links to GitHub Sponsors profiles on sponsorship, as well as …

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Classifying Source Code: How Far Can Compressor-Based Classifiers Go?, Zhou Yang Apr 2024

Classifying Source Code: How Far Can Compressor-Based Classifiers Go?, Zhou Yang

Research Collection School Of Computing and Information Systems

Pre-trained language models of code, which are built upon large-scale datasets, millions of trainable parameters, and high computational resources cost, have achieved phenomenal success. Recently, researchers have proposed a compressor-based classifier (Cbc); it trains no parameters but is found to outperform BERT. We conduct the first empirical study to explore whether this lightweight alternative can accurately classify source code. Our study is more than applying Cbc to code-related tasks. We first identify an issue that the original implementation overestimates Cbc. After correction, Cbc's performance on defect prediction drops from 80.7% to 63.0%, which is still comparable to CodeBERT (63.7%). We …

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Going Viral: Case Studies On The Impact Of Protestware, Youmei Fan, Dong Wang, Supastsara Wattanakriengkrai, Hathaichanok Damrongsiri, Christoph Treude, Hideaki Hata, Raula Gaikovina Kula Apr 2024

Going Viral: Case Studies On The Impact Of Protestware, Youmei Fan, Dong Wang, Supastsara Wattanakriengkrai, Hathaichanok Damrongsiri, Christoph Treude, Hideaki Hata, Raula Gaikovina Kula

Research Collection School Of Computing and Information Systems

Maintainers are now self-sabotaging their work in order to take political or economic stances, a practice referred to as "protestware". In this poster, we present our approach to understand how the discourse about such an attack went viral, how it is received by the community, and whether developers respond to the attack in a timely manner. We study two notable protestware cases, i.e., Colors.js and es5-ext, comparing with discussions of a typical security vulnerability as a baseline, i.e., Ua-parser, and perform a thematic analysis of more than two thousand protest-related posts to extract the different narratives when discussing protestware.

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Creative And Correct: Requesting Diverse Code Solutions From Ai, Scott Blyth, Markus Wagner, Christoph Treude Apr 2024

Creative And Correct: Requesting Diverse Code Solutions From Ai, Scott Blyth, Markus Wagner, Christoph Treude

Research Collection School Of Computing and Information Systems

AI foundation models have the capability to produce a wide array of responses to a single prompt, a feature that is highly beneficial in software engineering to generate diverse code solutions. However, this advantage introduces a significant trade-off between diversity and correctness. In software engineering tasks, diversity is key to exploring design spaces and fostering creativity, but the practical value of these solutions is heavily dependent on their correctness. Our study systematically investigates this trade-off using experiments with HumanEval tasks, exploring various parameter settings and prompting strategies. We assess the diversity of code solutions using similarity metrics from the code …

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Enhancing Source Code Representations For Deep Learning With Static Analysis, Xueting Guan, Christoph Treude Apr 2024

Enhancing Source Code Representations For Deep Learning With Static Analysis, Xueting Guan, Christoph Treude

Research Collection School Of Computing and Information Systems

Deep learning techniques applied to program analysis tasks such as code classification, summarization, and bug detection have seen widespread interest. Traditional approaches, however, treat programming source code as natural language text, which may neglect significant structural or semantic details. Additionally, most current methods of representing source code focus solely on the code, without considering beneficial additional context. This paper explores the integration of static analysis and additional context such as bug reports and design patterns into source code representations for deep learning models. We use the Abstract Syntax Tree-based Neural Network (ASTNN) method and augment it with additional context information …

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Test Optimization In Dnn Testing: A Survey, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon Apr 2024

Test Optimization In Dnn Testing: A Survey, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon

Research Collection School Of Computing and Information Systems

This article presents a comprehensive survey on test optimization in deep neural network (DNN) testing. Here, test optimization refers to testing with low data labeling effort. We analyzed 90 papers, including 43 from the software engineering (SE) community, 32 from the machine learning (ML) community, and 15 from other communities. Our study: (i) unifies the problems as well as terminologies associated with low-labeling cost testing, (ii) compares the distinct focal points of SE and ML communities, and (iii) reveals the pitfalls in existing literature. Furthermore, we highlight the research opportunities in this domain.

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Exploring The Potential Of Chatgpt In Automated Code Refinement: An Empirical Study, Guo Qi, Junming Cao, Xiaofei Xie, Shangqing Liu, Xiaohong Li, Bihuan Chen, Xin Peng Apr 2024

Exploring The Potential Of Chatgpt In Automated Code Refinement: An Empirical Study, Guo Qi, Junming Cao, Xiaofei Xie, Shangqing Liu, Xiaohong Li, Bihuan Chen, Xin Peng

Research Collection School Of Computing and Information Systems

Code review is an essential activity for ensuring the quality and maintainability of software projects. However, it is a time-consuming and often error-prone task that can significantly impact the development process. Recently, ChatGPT, a cutting-edge language model, has demonstrated impressive performance in various natural language processing tasks, suggesting its potential to automate code review processes. However, it is still unclear how well ChatGPT performs in code review tasks. To fill this gap, in this paper, we conduct the first empirical study to understand the capabilities of ChatGPT in code review tasks, specifically focusing on automated code refinement based on given …

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Minimon: Minimizing Android Applications With Intelligent Monitoring-Based Debloating, Jiakun Liu, Zicheng Zhang, Xing Hu, Thung Ferdian, Shahar Maoz, Debin Gao, Eran Toch, Zhipeng Zhao, David Lo Apr 2024

Minimon: Minimizing Android Applications With Intelligent Monitoring-Based Debloating, Jiakun Liu, Zicheng Zhang, Xing Hu, Thung Ferdian, Shahar Maoz, Debin Gao, Eran Toch, Zhipeng Zhao, David Lo

Research Collection School Of Computing and Information Systems

The size of Android applications is getting larger to fulfill the requirements of various users. However, not all the features of the applications are needed and desired by a specific user. The unnecessary and non-desired features can increase the attack surface and consume system resources such as storage and memory. To address this issue, we propose a framework, MiniMon, to debloat unnecessary features from an Android app based on the logs of specific users' interactions with the app.However, rarely used features may not be recorded during the data collection, and users' preferences may change slightly over time. To address these …

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Assessing Ai Detectors In Identifying Ai-Generated Code: Implications For Education, Wei Hung Pan, Ming Jie Chok, Jonathan Leong Shan Wong, Yung Xin Shin, Yeong Shian Poon, Zhou Yang, Chun Yong Chong, David Lo, Mei Kuan Lim Apr 2024

Assessing Ai Detectors In Identifying Ai-Generated Code: Implications For Education, Wei Hung Pan, Ming Jie Chok, Jonathan Leong Shan Wong, Yung Xin Shin, Yeong Shian Poon, Zhou Yang, Chun Yong Chong, David Lo, Mei Kuan Lim

Research Collection School Of Computing and Information Systems

Educators are increasingly concerned about the usage of Large Language Models (LLMs) such as ChatGPT in programming education, particularly regarding the potential exploitation of imperfections in Artificial Intelligence Generated Content (AIGC) Detectors for academic misconduct.In this paper, we present an empirical study where the LLM is examined for its attempts to bypass detection by AIGC Detectors. This is achieved by generating code in response to a given question using different variants. We collected a dataset comprising 5,069 samples, with each sample consisting of a textual description of a coding problem and its corresponding human-written Python solution codes. These samples were …

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Large Language Model For Vulnerability Detection: Emerging Results And Future Directions, Xin Zhou, Ting Zhang, David Lo Apr 2024

Large Language Model For Vulnerability Detection: Emerging Results And Future Directions, Xin Zhou, Ting Zhang, David Lo

Research Collection School Of Computing and Information Systems

Previous learning-based vulnerability detection methods relied on either medium-sized pre-trained models or smaller neural networks from scratch. Recent advancements in Large Pre-Trained Language Models (LLMs) have showcased remarkable few-shot learning capabilities in various tasks. However, the effectiveness of LLMs in detecting software vulnerabilities is largely unexplored. This paper aims to bridge this gap by exploring how LLMs perform with various prompts, particularly focusing on two state-of-the-art LLMs: GPT-3.5 and GPT-4. Our experimental results showed that GPT-3.5 achieves competitive performance with the prior state-of-the-art vulnerability detection approach and GPT-4 consistently outperformed the state-of-the-art.

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Code Search Is All You Need? Improving Code Suggestions With Code Search, Junkai Chen, Xing Hu, Zhenhao Li, Cuiyun Gao, Xin Xia, David Lo Apr 2024

Code Search Is All You Need? Improving Code Suggestions With Code Search, Junkai Chen, Xing Hu, Zhenhao Li, Cuiyun Gao, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

Modern integrated development environments (IDEs) provide various automated code suggestion techniques (e.g., code completion and code generation) to help developers improve their efficiency. Such techniques may retrieve similar code snippets from the code base or leverage deep learning models to provide code suggestions. However, how to effectively enhance the code suggestions using code retrieval has not been systematically investigated. In this paper, we study and explore a retrieval-augmented framework for code suggestions. Specifically, our framework leverages different retrieval approaches and search strategies to search similar code snippets. Then the retrieved code is used to further enhance the performance of language …

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Greening Large Language Models Of Code, Jieke Shi, Zhou Yang, Hong Jin Kang, Bowen Xu, Junda He, David Lo Apr 2024

Greening Large Language Models Of Code, Jieke Shi, Zhou Yang, Hong Jin Kang, Bowen Xu, Junda He, David Lo

Research Collection School Of Computing and Information Systems

Large language models of code have shown remarkable effectiveness across various software engineering tasks. Despite the availability of many cloud services built upon these powerful models, there remain several scenarios where developers cannot take full advantage of them, stemming from factors such as restricted or unreliable internet access, institutional privacy policies that prohibit external transmission of code to third-party vendors, and more. Therefore, developing a compact, efficient, and yet energy-saving model for deployment on developers' devices becomes essential.To this aim, we propose Avatar, a novel approach that crafts a deployable model from a large language model of code by optimizing …

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Ps3: Precise Patch Presence Test Based On Semantic Symbolic Signature, Qi Zhan, Xing Hu, Zhiyang Li, Xin Xia, David Lo, Shanping Li Apr 2024

Ps3: Precise Patch Presence Test Based On Semantic Symbolic Signature, Qi Zhan, Xing Hu, Zhiyang Li, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

During software development, vulnerabilities have posed a significant threat to users. Patches are the most effective way to combat vulnerabilities. In a large-scale software system, testing the presence of a security patch in every affected binary is crucial to ensure system security. Identifying whether a binary has been patched for a known vulnerability is challenging, as there may only be small differences between patched and vulnerable versions. Existing approaches mainly focus on detecting patches that are compiled in the same compiler options. However, it is common for developers to compile programs with very different compiler options in different situations, which …

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Coca: Improving And Explaining Graph Neural Network-Based Vulnerability Detection Systems, Sicong Cao, Xiaobing Sun, Xiaoxue Wu, David Lo, Lili Bo, Bin Li, Wei Liu Apr 2024

Coca: Improving And Explaining Graph Neural Network-Based Vulnerability Detection Systems, Sicong Cao, Xiaobing Sun, Xiaoxue Wu, David Lo, Lili Bo, Bin Li, Wei Liu

Research Collection School Of Computing and Information Systems

Recently, Graph Neural Network (GNN)-based vulnerability detection systems have achieved remarkable success. However, the lack of explainability poses a critical challenge to deploy black-box models in security-related domains. For this reason, several approaches have been proposed to explain the decision logic of the detection model by providing a set of crucial statements positively contributing to its predictions. Unfortunately, due to the weakly-robust detection models and suboptimal explanation strategy, they have the danger of revealing spurious correlations and redundancy issue.In this paper, we propose Coca, a general framework aiming to 1) enhance the robustness of existing GNN-based vulnerability detection models to …

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Ppt4j: Patch Presence Test For Java Binaries, Zhiyuan Pan, Xing Hu, Xin Xia, Xian Zhan, David Lo, Xiaohu Yang Apr 2024

Ppt4j: Patch Presence Test For Java Binaries, Zhiyuan Pan, Xing Hu, Xin Xia, Xian Zhan, David Lo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results.In this paper, we propose a new patch presence test framework named Ppt4J …

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Streamlining Java Programming: Uncovering Well-Formed Idioms With Idiomine, Yanming Yang, Xing Hu, Xin Xia, David Lo, Xiaohu Yang Apr 2024

Streamlining Java Programming: Uncovering Well-Formed Idioms With Idiomine, Yanming Yang, Xing Hu, Xin Xia, David Lo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Code idioms are commonly used patterns, techniques, or practices that aid in solving particular problems or specific tasks across multiple software projects. They can improve code quality, performance, and maintainability, and also promote program standardization and reuse across projects. However, identifying code idioms is significantly challenging, as existing studies have still suffered from three main limitations. First, it is difficult to recognize idioms that span non-contiguous code lines. Second, identifying idioms with intricate data flow and code structures can be challenging. Moreover, they only extract dataset-specific idioms, so common idioms or well-established code/design patterns that are rarely found in datasets …

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Deep Reinforcement Learning For Dynamic Algorithm Selection: A Proof-Of-Principle Study On Differential Evolution, Hongshu Guo, Yining Ma, Zeyuan Ma, Jiacheng Chen, Xinglin Zhang, Zhiguang Cao, Jun Zhang, Yue-Jiao Gong Apr 2024

Deep Reinforcement Learning For Dynamic Algorithm Selection: A Proof-Of-Principle Study On Differential Evolution, Hongshu Guo, Yining Ma, Zeyuan Ma, Jiacheng Chen, Xinglin Zhang, Zhiguang Cao, Jun Zhang, Yue-Jiao Gong

Research Collection School Of Computing and Information Systems

Evolutionary algorithms, such as differential evolution, excel in solving real-parameter optimization challenges. However, the effectiveness of a single algorithm varies across different problem instances, necessitating considerable efforts in algorithm selection or configuration. This article aims to address the limitation by leveraging the complementary strengths of a group of algorithms and dynamically scheduling them throughout the optimization progress for specific problems. We propose a deep reinforcement learning-based dynamic algorithm selection framework to accomplish this task. Our approach models the dynamic algorithm selection a Markov decision process, training an agent in a policy gradient manner to select the most suitable algorithm according …

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Concretely Mapped Symbolic Memory Locations For Memory Error Detection, Haoxin Tu, Lingxiao Jiang, Jiaqi Hong, Xuhua Ding, He Jiang Apr 2024

Concretely Mapped Symbolic Memory Locations For Memory Error Detection, Haoxin Tu, Lingxiao Jiang, Jiaqi Hong, Xuhua Ding, He Jiang

Research Collection School Of Computing and Information Systems

Memory allocation is a fundamental operation for managing memory objects in many programming languages. Misusing allocated memory objects (e.g., buffer overflow and use-after-free) can lead to catastrophic consequences. Symbolic execution-based approaches are often used to detect such memory errors, leveraging their capabilities in automatic path exploration and test case generation. However, existing symbolic execution engines face significant limitations in modeling dynamic memory layouts. These engines either represent memory object locations as concrete addresses, limiting analyses to specific address layouts and missing errors that occur at special addresses, or represent locations as simple symbolic variables without sufficient constraints, resulting in memory …

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Adaptive Content-Aware Influence Maximization Via Online Learning To Rank, Konstantinos Theocharidis, Panagiotis Karras, Manolis Terrovitis, Spiros Skiadopoulos, Hady Wirawan Lauw Apr 2024

Adaptive Content-Aware Influence Maximization Via Online Learning To Rank, Konstantinos Theocharidis, Panagiotis Karras, Manolis Terrovitis, Spiros Skiadopoulos, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

How can we adapt the composition of a post over a series of rounds to make it more appealing in a social network? Techniques that progressively learn how to make a fixed post more influential over rounds have been studied in the context of the Influence Maximization (IM) problem, which seeks a set of seed users that maximize a post’s influence. However, there is no work on progressively learning how a post’s features affect its influence. In this article, we propose and study the problem of Adaptive Content-Aware Influence Maximization (ACAIM), which calls to find k features to form a …

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Filter-Based Stance Network For Rumor Verification, Jun Li, Yi Bin, Yunshan Ma, Yang Yang, Zi Huang, Tat‑Seng Chua Apr 2024

Filter-Based Stance Network For Rumor Verification, Jun Li, Yi Bin, Yunshan Ma, Yang Yang, Zi Huang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Rumor verification on social media aims to identify the truth value of a rumor, which is important to decreasethe detrimental public effects. A rumor might arouse heated discussions and replies, conveying differentstances of users that could be helpful in identifying the rumor. Thus, several works have been proposedto verify a rumor by modelling its entire stance sequence in the time domain. However, these works ignorethat such a stance sequence could be decomposed into controversies with different intensities, which could beused to cluster the stance sequences with the same consensus. In addition, the existing stance extractors fail toconsider both the impact …

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Hop‑Based Heterogeneous Graph Transformer, Zixuan Yang, Xiao Wang, Yanhua Yu, Yuling Wang, Kangkang Lu, Zirui Guo, Xiting Qin, Yunshan Ma, Tat‑Seng Chua Apr 2024

Hop‑Based Heterogeneous Graph Transformer, Zixuan Yang, Xiao Wang, Yanhua Yu, Yuling Wang, Kangkang Lu, Zirui Guo, Xiting Qin, Yunshan Ma, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

The Graph Transformer (GT) has shown significant ability in processing graph-structured data, addressing limitations in graph neural networks, such as over-smoothing and over-squashing. However, the implementation of GT in real-world heterogeneous graphs (HGs) with complex topology continues to present numerous challenges. Firstly, a challenge arises in designing a tokenizer that is compatible with heterogeneity. Secondly, the complexity of the transformer hampers the acquisition of high-order neighbor information in HGs. In this paper, we propose a novel Hop-basedHeterogeneous Graph Transformer (H2Gormer) framework, paving a promising path for HGs to benefit from the capabilities of Transformers. We propose a Heterogeneous Hop-based Token …

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A Community Centered Culturally Responsive Evaluation Of Our Behavioral Health Team Using Program Theory, Debra Ann Fritz-Fanning Apr 2024

A Community Centered Culturally Responsive Evaluation Of Our Behavioral Health Team Using Program Theory, Debra Ann Fritz-Fanning

Theses and Dissertations

The purpose of this research was to conduct a culturally responsive evaluation of our behavioral health team using program theory. This team consists of both school-based employees and community partners. The goal of this project was to evaluate our current behavioral health team practices to determine if we are improving the mental health of students at our school by intentionally connecting them with services. Program theory was used to design the evaluation, and members of the team engaged in six focus groups to create and evaluate the program. The team evaluated three areas of our work: organizational structures, resources, and …

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Arcadia Magazine, 2024-04 Apr 2024

Arcadia Magazine, 2024-04

Alumni Magazine

No abstract provided.

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Balancing Wellness And Leadership: Exploring Black Women Administrators’ Subjective Well-Being, Resilience, And Radical Self-Care In Higher Education, Lashae Grottis Apr 2024

Balancing Wellness And Leadership: Exploring Black Women Administrators’ Subjective Well-Being, Resilience, And Radical Self-Care In Higher Education, Lashae Grottis

Electronic Theses and Dissertations

Leaders in higher education experience high and unrealistic demands for their skills, time, and energy, causing stress, competing priorities, burnout, compromised health, and attrition. However, unlike other racial and gender groups, Black women higher education administrators experienced these challenges more intensely. As a result of chronic stress associated with being undervalued and overworked, discriminatory and unwelcoming workplaces, and intersectional biases, Black women leaders are leaving higher education workplaces. Despite the link between gendered racism and unwellness, little is known about the problem from a positive leadership perspective. This study addressed the lack of knowledge of the wellness strategies Black women …

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The Correlational Effects Of Academic And Demographic Factors On Nursing Students’ Attrition, Progression, And Completion At A University College Of Nursing, Adebusola A. Obafemi Apr 2024

The Correlational Effects Of Academic And Demographic Factors On Nursing Students’ Attrition, Progression, And Completion At A University College Of Nursing, Adebusola A. Obafemi

Electronic Theses and Dissertations

High United States nursing student attrition rates have attracted political, organizational, and social interest for numerous reasons. This attrition places financial burdens on the students, results in revenue loss for the college, and exacerbates the existing nursing shortage. Students' success in a nursing program is crucial for nurses to practice nursing. The purpose of this retrospective study was to explore the effects of academic performance and demographic/categorical factors on nursing students' progression and completion at a University College of Nursing in the Southern United States. The target population consisted of students enrolled at the University College of Nursing from January …

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Best Practices In Professional Learning Communities That Influence Teacher Self-Efficacy, Silian Molly Hornsby Apr 2024

Best Practices In Professional Learning Communities That Influence Teacher Self-Efficacy, Silian Molly Hornsby

Electronic Theses and Dissertations

This qualitative case study examined best practices in professional learning communities (PLCs) that influence teacher self-efficacy. Participants were primary and intermediate teachers and teacher leaders from a large suburban elementary school in Texas. Participants had to have at least 1 year of teaching and PLC experience. The semistructured teacher and teacher-leader interviews were recorded and transcribed in real time using Otter.ai software. After members checked the transcription, the transcription was uploaded into the NVivo software for coding and identifying common themes. The findings were separated into themes based on the study’s research questions. Research question 1 explored what teachers and …

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Harnessing The Power Of Cliftonstrengths®: How Multinational Corporations Can Use Deep-Level Diversity To Enhance Organizational Inclusion, Trapper Kay Pace Apr 2024

Harnessing The Power Of Cliftonstrengths®: How Multinational Corporations Can Use Deep-Level Diversity To Enhance Organizational Inclusion, Trapper Kay Pace

Electronic Theses and Dissertations

This research explicitly investigated how multinational corporations can enhance workplace inclusion through the novel use of the CliftonStrengths® assessment as a dimension of deep-level diversity. The study gleaned insights from employees’ perspectives, employing a constructivist grounded theory approach to explicate their experiences in rich qualitative narratives. Through open-ended surveys and intensive interviews, participants were selected using purposeful sampling to ensure meaningful data collection from the study organizations’ three global regions. The researcher conducted the analysis systematically through the constant comparison of data utilizing the NVivo14 software to assist in constructing codes, themes, and a theoretical schema. Results highlighted the significance …

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